Cardiac catheterization represents a significant medical diagnostic or treatment exposure risk for the development of acute kidney injury (AKI). That risk varies widely depending on the patient's pre-procedural medical conditions as well as exposures during or immediately before or after the procedure. Post procedural AKI occurs in 1% to 31% of the patients, depending on the cohort studied, and is associated with a 30% one- year mortality rate. For outcomes further downstream, AKI increases the risk of progressing to chronic kidney disease, which can lead to dialysis, increased cardiovascular adverse outcomes, reductions in quality of life, and significant personal and health care costs. Cardiac catheterization is a high risk, closely observed, and intervenable clinical care window, and preventing an occurrence of AKI would have significant impact on veteran's health and VA costs of care, which has been estimated to be approximately $7,500 per patient. However, automated outcomes surveillance is not widely performed, and the VA does not currently have the informatics tools to conduct this surveillance for the 40,000 veterans a year undergoing the procedure. The overall objective of this project is to develop the infrastructure and tools to perform national VA near real-time automated adverse event surveillance after cardiac catheterization, and to demonstrate the utility of the tools within the use case of post-procedural AKI. More specifically, we will 1) develop and validate near real-time natural language processing (NLP) tools using interactive learning techniques in order to extract information that is relevant to AKI but is collected in structured data, 2) develop and validate a robust family of logistic regression prediction models for AKI following cardiac catheterization for use in the identification of high risk patients and populations, and 3) conduct automated national retrospective and prospective analyses of institutional care variation among veterans receiving cardiac catheterization using novel surveillance methods. This proposal will analyze retrospective and prospective cohort data from the VA Cardiovascular Assessment, Reporting, and Tracking for Catheterization Laboratories (CART-CL) voluntary clinical registry and electronic health record system (CPRS) from 2009 to 2015. All adult patients who received a cardiac catheterization in the VA during this time period will be included. All variables will be extracted from the structured data elements of CART-CL and CPRS, with near real time NLP used to extract risk factors from unstructured data. Risk factors will be identified by comprehensive literature review, expert consensus, and discovery during evaluation of retrospective signals, and selected through the use of the Lasso regression variable selection technique. Logistic regression models will be developed for each of the Acute Kidney Injury Network AKI stages, internally validated with bootstrapping, and externally validated with the Northern New England Cardiovascular Disease Study Group percutaneous coronary intervention registry. Institutional surveillance analyses will be conducted using maximized sequential probability ratio testing and Bayesian hierarchical logistic regression. The strongest institutional outliers will have manual case review of patient cases that experienced the outcome in order to ascertain key clinical care variations. A governance board consisting of CART and VA interventional cardiology leaders will be established to supervise detected signals for identification and feedback to individual institutions. This proposal will improve veterans' care n a number of areas. This work has the potential to discover new risk factors associated with AKI, to provide robust risk stratification tools for the identification of high risk patients prior to te procedure, and allow the detection of institutional outliers and clinical care process variation tht is associated with increased AKI risk that may be amenable to quality improvement interventions. Finally, the informatics infrastructure and NLP development has the potential to be applied in a wide variety of exposures and outcomes beyond AKI for cardiac catheterization surveillance.